flowchart LR
A[Raw FASTQ] --> B[QC & Trimming]
B --> C[Alignment]
C --> D[Quantification]
D --> E[Differential Expression]
E --> F[Pathway Analysis]
style A fill:#e74c3c,color:white
style B fill:#f39c12,color:white
style C fill:#3498db,color:white
style D fill:#9b59b6,color:white
style E fill:#1abc9c,color:white
style F fill:#27ae60,color:white
Bulk RNA-seq Pipeline
From raw reads to differential expression and pathway analysis
Overview
This pipeline provides a comprehensive workflow for analyzing bulk RNA-sequencing data. It covers everything from quality control of raw reads to differential gene expression analysis and functional enrichment.
Pipeline Steps
1. Preprocessing & Quality Control Read More →
Quality assessment with FastQC/MultiQC and adapter trimming with fastp or Trimmomatic.
2. Read Alignment Read More →
Alignment to reference genome using STAR or HISAT2.
3. Quantification Read More →
Gene-level count quantification using featureCounts or Salmon.
4. Differential Expression Analysis Read More →
Statistical analysis of differential gene expression using DESeq2 or edgeR.
5. Pathway & Functional Analysis Read More →
Gene set enrichment analysis and pathway visualization.
References
- Love MI, Huber W, Anders S (2014). “Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.” Genome Biology, 15:550.
- Wu T, et al. (2021). “clusterProfiler 4.0: A universal enrichment tool for interpreting omics data.” The Innovation, 2(3):100141.